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Merge pull request #225 from mihaimorariu/devel/qm9_example
Update README in the QM9 example
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examples/qm9/README.md

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# QM9
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# QM9 Regression Example
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QM9 dataset regression
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This example performs regression on the QM9 dataset.
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## Dependency
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## Dependencies
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This example depends on the following package as well as
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Chainer Chemistry and its dependent packages:
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To run the example, the following packages also need to be installed:
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- [`matplotlib`](https://matplotlib.org/)
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- [`seaborn`](https://seaborn.pydata.org/)
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- [`scikit-learn`](http://scikit-learn.org/stable/)
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## How to run the code
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### Train the model with qm9 dataset
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### Training a model
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With CPU:
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To train a model, run the following:
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On the CPU:
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```angular2html
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python train_qm9.py
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```
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With GPU:
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On the GPU:
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```angular2html
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python train_qm9.py -g 0
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```
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### Inference with the trained model with qm9 dataset using Regressor
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As of v0.3.0, `Regressor` class is introduced which supports `predict`
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method for easier inference.
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### Inference using a pretrained model
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`Regressor` also supports `load_pickle` method, user may load
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the instance of pretrained-model using `pickle` file.
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As of v0.3.0, the `Regressor` class has been introduced, which provides the
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`predict` method for easier inference. `Regressor` also supports the
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`load_pickle` method, which allows for loading of a pretrained model, using the
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`pickle` library.
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The example implemented in `predict_qm9.py`.
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The perform inference using a pretrained model, run the following:
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With CPU:
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On the CPU:
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```
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python predict_qm9.py [-i /path/to/training/result/directory]
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```
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With GPU:
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On the GPU:
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```
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python predict_qm9.py -g 0 [-i /path/to/training/result/directory]
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```

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